Videos Archive - PrestoDB

Keynote Panel: Presto at Scale – Shradha Ambekar, Gurmeet Singh, Neerad Somanchi & Rupa Gangatirkar

Over the last decade Presto has become one of the most widely adopted open source SQL query engines. In use at companies large and small, Presto’s performance, reliability, and efficiency at scale have become critical to many companies’ data infrastructures. In this panel we’ll hear from three of the largest companies running Presto at scale – Meta, Uber, and Intuit. They’ll share more about their learnings, some of their impressive performance metrics with Presto, and what they envision going forward for Presto at their respective companies.

Presto SQL Functions – Facebook

In this talk we will show how to use the recently introduced SQL function feature, how it works, and the ongoing work to support invoking arbitrary functions remotely with remote UDF server.

Dynamic UDF Framework and its Applications – Rongrong Zhong, Alluxio & Yanbing Zhang, Bytedance

Presto supports dynamically registered User Defined Functions (UDFs) since 2020. Over the years, we used this framework to add support for SQL UDFs and remote / external UDFs. One common community request in the UDF domain is to support Hive UDFs. Many companies have legacy Hive pipelines, and engineers who are familiar with HQL and Hive UDFs. With remote UDF, one can implement Hive UDF support as UDFs running on the remote cluster. But since HiveUDFs are written in Java, we can also run them inside the engine. We extended the dynamic UDF framework to support Java UDFs, and used this new extension to add HiveUDF support in Presto. With this feature, users can directly use their familiar HiveUDFs and UDAFs in their Presto query.

Presto On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit

Presto on Spark is an integration between Presto and Spark that leverages Presto’s compiler/evaluation as a library and Spark’s large scale processing capabilities. It enables a unified SQL experience between interactive and batch use cases. A unified option for batch data processing and ad hoc is very important for creating the experience of queries that scale instead of fail without requiring rewrites between different SQL dialects. In this session, we’ll talk about Presto On Spark architecture, why it matters and its implementation/usage at Intuit.

(Chinese) Presto at Bytedance – Hive UDF Wrapper for Presto

Presto has been widely used at Bytedance in several ways such as in the data warehouse, BI tools, ads etc. And, the Presto team at Bytedance has also delivered many key features and optimizations such as the Hive UDF wrapper, coordinator, runtime filter and so on which extend Presto usages and enhance Presto stabilities. Nowadays, most companies will use both Hive (or Spark) and Presto together. But Presto UDFs have very different syntax and internal mechanisms compared with Hive UDFs. This restricts Presto usage while users need to maintain 2 kinds of functions. In this talk, we will present a way to execute Hive UDF/UDAF inside Presto.